State-of-the-Art Technologies for Development of High Frequency Transformers with Advanced Magnetic Materials

Sarker, Pejush Chandra; Islam, Md. Rabiul; Guo, Youguang; Zhu, Jun; Lu, Hanqing

doi:10.1109/tasc.2018.2882411

Cited by 40 publications

(21 citation statements)

References 69 publications

(90 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is the basis for models such as the modified Steinmetz equation [34], generalized Steinmetz equation [16], the improved generalized Steinmetz equation (iGSE) [40], and the improved improved generalized Steinmetz equation [26]. Further discussion on this topic is available in [18], [41], [42]. For its simplicity, iGSE remains the preferred method for core loss calculations.…”

Section: Impact Of Duty Cycle In Triangular Waveformsmentioning

confidence: 99%

Why MagNet: Quantifying the Complexity of Modeling Power Magnetic Material Characteristics

Chen¹

2023

Preprint

View full text Add to dashboard Cite

<p>Core losses and hysteresis loops are critical information in the design process of power magnetics, yet the physics behind core loss and hysteresis loops are not fully understood. Core losses and hysteresis loops change for each magnetic material, are highly nonlinear, and depend heavily on the electrical operating conditions (e.g., waveform, frequency, amplitude, dc bias) and the mechanical properties (e.g. pressure, vibration), as well as temperature, and geometry of the magnetic components. Understanding the complexity of these factors is important for the development of accurate models, in addition to addressing the applicability and limitations of each model. Existing studies on power magnetics are usually developed based on a small amount of data and do not reveal the full magnetic behavior across a wide range of operating conditions. In this paper, based on a recently developed large-scale open-source database - MagNet - the core losses and hysteresis loops of Mn-Zn ferrites are analyzed in a wide range of amplitudes, frequencies, waveform shapes, dc bias, and temperatures, to quantify the complexity of modeling magnetic core losses and hysteresis loops and provide guidelines for modeling power magnetics with data-driven methods.</p>

show abstract

Section: Impact Of Duty Cycle In Triangular Waveformsmentioning

confidence: 99%

Why MagNet: Quantifying the Complexity of Modeling Power Magnetic Material Characteristics

Chen¹

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Figure 1c shows a prototype of a high-frequency transformer with Litz-wire windings and a magnetic core made of nanocrystalline films. High-frequency transformers have many potential and promising applications, including in the power systems and wireless power transmission systems [26][27][28]. For the design optimization of a high-frequency transformer, there are many objectives, such as minimizing the loss and volume.…”

Section: Deterministic Design Optimizationmentioning

confidence: 99%

Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

Lei

Bramerdorfer

et al. 2021

Applied Sciences

Self Cite

View full text Add to dashboard Cite

This paper reviews the recent developments of design optimization methods for electromagnetic devices, with a focus on machine learning methods. First, the recent advances in multi-objective, multidisciplinary, multilevel, topology, fuzzy, and robust design optimization of electromagnetic devices are overviewed. Second, a review is presented to the performance prediction and design optimization of electromagnetic devices based on the machine learning algorithms, including artificial neural network, support vector machine, extreme learning machine, random forest, and deep learning. Last, to meet modern requirements of high manufacturing/production quality and lifetime reliability, several promising topics, including the application of cloud services and digital twin, are discussed as future directions for design optimization of electromagnetic devices.

show abstract

“…Some design restrictions are listed to follow: maximum power, stability, efficiency, input and output voltage levels, operation frequency, thermal stability, system cooling, portability, energy losses and cost [1], [78]. Table 6 summaries the most relevant parameters [52], [61], [69], [77], [78], [89], [99], [107], [108]. Please note that parameters showed in Table 7 are provided for a specific alloy and its manufacturer provider.…”

Section: Comparison Of Ferromagnetic Materials Used In Pecmentioning

confidence: 99%

Advanced Ferromagnetic Materials in Power Electronic Converters: A State of the Art

et al. 2020

View full text Add to dashboard Cite

Currently, the design of power electronic converters (PECs) is in a stage which looks increasing its efficiency and reducing its dimensions from basic topologies through precise analysis of losses in each device that conforms. Although there have been important advances to improve the active components of PECs, passive components, particularly inductors, have changed very little since several decades ago, and those begin a limiting factor, when a high efficiency at very high power and switching frequency, are required. It is for the foregoing that this paper reviews new ferromagnetic materials to construct inductors and achieve a substantial increasing in efficiency through magnetic permeability and hysteresis improvements. Therefore, the main objective of this work is to position the reader in the state of the art of advanced ferromagnetic materials used in PECs. Details about how they are constructed and qualitative comparison of their dynamic behavior are provided. Also, estimation methods of energy losses, applications for different types of material, and its influence on the performance of some basic PECs are presented. INDEX TERMS Amorphous magnetic materials, magnetic materials, power conversion, soft magnetic materials.

show abstract

State-of-the-Art Technologies for Development of High Frequency Transformers with Advanced Magnetic Materials

Cited by 40 publications

References 69 publications

Why MagNet: Quantifying the Complexity of Modeling Power Magnetic Material Characteristics

Why MagNet: Quantifying the Complexity of Modeling Power Magnetic Material Characteristics

Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

Advanced Ferromagnetic Materials in Power Electronic Converters: A State of the Art

Contact Info

Product

Resources

About